Betterworks: Why Poor AI Use Could Make Performance Management Worse

Summary

This article addresses the risk that AI integration in performance management systems may increase rather than reduce complexity for end users. The central argument, attributed to Bruce Walcroft, Director of Solutions Engineering at Betterworks, is that many organizations layer AI onto unchanged processes and expect different outcomes — a strategy that reliably fails. Key evidence is anecdotal and practitioner-sourced: Walcroft cites the cognitive burden of prompt-writing, the challenge of non-native language users completing performance reviews, and the tendency of enterprise vendors to underestimate implementation complexity. Betterworks is presented as advocating an embedded 'copilot' model of AI — one that operates in the background, reduces administrative burden on managers, and supports goal alignment with organizational strategy. The article also highlights a claimed internal ratio of 20% technology to 80% change management in Betterworks implementations. The implied conclusion is that AI's value in performance management is contingent on workflow redesign and user adoption strategy, not technological sophistication alone. The piece was produced in the context of HR Tech Europe 2026.

Strengths and limitations

The article presents a coherent and broadly credible argument about AI implementation failure modes in performance management. The core thesis — that AI must reduce complexity rather than repackage it — is consistent with independent research on technology adoption. However, the article is structurally promotional: the sole named source is a Betterworks employee, no independent voices or competing perspectives are included, and Betterworks' own approach is presented as the implicit solution to every problem identified. The 80/20 change management statistic is attributed to Betterworks internally with no external validation or sourcing. There is no empirical data, no referenced studies, and no client outcomes cited. The framing as a journalistic interview from a trade media outlet (UC Today) lends surface credibility, but the content functions as vendor thought leadership. Limitations include absence of counterargument, no discussion of cases where AI has succeeded without heavy change management investment, and no acknowledgment of Betterworks' commercial interest in the conclusions drawn.

What this implies

The article reflects a broader industry tension between AI as a marketing differentiator and AI as a functional workflow improvement. The emphasis on change management over technology investment, if accurate, suggests that competitive differentiation in the performance management software market may increasingly rest on implementation services and adoption support rather than feature sets alone. The characterization of most AI innovation as 'interface decoration' implies a market correction may be underway, where buyers prioritize measurable friction reduction over AI capability claims. The shift toward continuous, embedded performance practices — rather than episodic annual reviews — is presented as an emerging directional trend, contingent on AI tools that integrate into daily work rather than adding discrete process steps.

Key points

• AI layered onto unchanged performance management processes is unlikely to produce different outcomes — the underlying workflow must be redesigned for AI to deliver value.
• User adoption of AI tools in performance management is inhibited when complexity is shifted to the employee rather than removed — invisibility and simplicity are stronger drivers of uptake than novelty.
• Betterworks positions AI as a background copilot that refines user-initiated work rather than replacing human input, with implementation framed as 80% change management and 20% technology.

What to take away

• Organizations evaluating AI-enabled performance management platforms can use the 'complexity transfer' test: assess whether the tool reduces friction for end users or simply relocates it to a different point in the workflow.
• Implementation planning for AI in performance management involves proportioning effort toward organizational readiness and change management, not solely toward technology configuration — a ratio explicitly cited by the vendor in this article.